Although studies have investigated how students’ problem-solving strategies influence their performance, few explore the comparison between high and low performers. Moreover, there is a lack of empirical evidence on the effect of students’ problem-solving strategies on their performance in visual programming systems. To address this research gap, we developed a visual programming system based on the self-regulated learning model, providing top-down and bottom-up perspectives. We recruited 35 university students to use the visual programming system and collected eye movement data to analyze their problem-solving strategies. Finally, 19 students’ data with the weighted gaze sampling rate exceeding 80% were used to ensure robust data reliability. The results revealed: (1) a positive correlation between performance and visits to the top-down tracking window, and a negative correlation with the bottom-up window; and (2) both high and low performers used both strategies, but high performers favored the top-down approach. The findings suggest that to better support low performers, the visual programming system should provide guidance on applying the top-down strategy for problem-solving. The implications of this study highlight the importance of problem-solving strategies in programming and suggest that incorporating visual scaffolding for the top-down approach may help low performers narrow the gap with high performers.
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